dplyr_workflow_compare_many_glm_models

gistfile1.txt

library(tidyverse)

set.seed(123)

# some random dataa
my_dat <- tibble(
  y = rbinom(300, 1, 0.5), # outcome 1
  z = rbinom(300, 1, 0.5), # outcome 2
  t = rbinom(300, 1, 0.5), # outcome 3
  indep_var = runif(300), # this is our independet variable
  x = sample(1:3, 300, replace = TRUE) # this is a grouping variable
)

# lets generate a meta data.frame containing all combinations ...
# ... of outcomes and groups of `x` and their models:
res <- tibble(dep = c("y", "z", "t")) %>%
  expand_grid(x = c(1:3)) %>% 
  rowwise() %>%
  mutate(mod = list(
    glm(reformulate("indep_var", dep),
        data = filter(my_dat, x == .env$x),
        family = binomial)
    )
    )

# compare different outcome, groups of x and their models
res %>% 
  mutate(fit = broom::glance(mod),
         .keep = "unused") %>% 
  unnest(fit)


# compare coefficients across different models
res %>% 
  summarise(dep,
            x, 
            broom::tidy(mod)) %>% 
  filter(term != "(Intercept)")